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相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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相关实验视频

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路径路由卷积和可扩展的轻量级网络,用于强大的水下声学目标识别.

Yue Zhao1, Menghan Chen2, Yuchen Lu2

  • 1School of Nautical Technology, Jiangsu Maritime Institute, Nanjing 211100, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
概括

这项研究引入了一种新的深度学习模型,用于识别使用水下声音识别船舶类型. 这种新的方法提高了在功率有限的海洋传感器上部署的精度和效率.

关键词:
轻量级神经网络是一种轻量级的神经网络.多尺度特征提取多尺度特征提取路径路由的卷积卷积.水下声学目标识别系统

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科学领域:

  • 海洋声学 海洋声学
  • 水下声学 水下声学
  • 信号处理 信号处理

背景情况:

  • 准确的船舶识别对于海上监视和海洋保护至关重要.
  • 目前用于水下声学识别的深度学习模型是计算密集型的,并与多尺度特征作斗争,限制它们在资源有限的设备上使用.

研究的目的:

  • 开发一个高效的深度学习模型,用于准确的水下船舶类型识别.
  • 解决现有模型在参数计数和多尺度特征提取方面的局限性.

主要方法:

  • 提出了一种新的路径路由卷积机制,具有多扩张率并行路径和自适应路由策略.
  • 设计了MobilePR-ConvNet架构,用于硬件适应性的系统宽度缩放.
  • 在DeepShip和ShipsEar数据集上进行了实验.

主要成果:

  • 在DeepShip上达到98.58%的高识别精度,在ShipsEar上达到97.82%.
  • 在低信号噪声比 (10dB) 条件下,具有77.8%的准确性,证明了强大的性能.
  • 在复杂的海洋环境中验证了跨数据集概括能力.

结论:

  • 拟议的MobilePR-ConvNet提供了一个有效的解决方案,用于在资源有限的水下设备上进行智能船舶识别.
  • 新的路径路由卷积机制使得跨度声学特征的歧视性提取成为可能.
  • 该模型显示出强大的性能和适应性,用于实际的海上监视应用.